Practical semiautomatic global registration of multiple point clouds based on semidefinite programming

Abstract

Multiple point clouds registration is a prerequisite step for geometry object observation from multiple viewpoints. Most algorithms depend on pose graph or relative poses, and they make the results ambiguous and inconsistent with the original point clouds. However, the existing algorithms based on original data can almost exclusively be applied to simulated data. In this paper, a practical semiautomatic global registration algorithm is proposed. A objective function is proposed; it utilizes correspondences between point cloud pairs directly to describe this problem, making the algorithm easy to interface with other point cloud matching algorithms. Moreover, it can be effectively approximated by semidefinite programming. Since no automatic algorithm can guarantee the correctness of the correspondences, a manual strategy is proposed to filter the correspondences and find the correlation between point clouds. Based on the correlation, irrelevant point clouds can be removed easily in the point cloud set or divide the set into multiple independent parts. Besides, a postprocessing strategy based on the correlation is proposed for refining the results. The qualitative and quantitative experimental results demonstrate that the approach achieves more accurate and robust performance than previous algorithms.